scholarly journals Abstract 999: Unravelling myeloid and T cell compartment interactions through a novel approach to tumor multimodal analysis combining whole slide multiplexed immunofluorescence and gene expression profiling

Author(s):  
Natalie Zwing
2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 10030-10030
Author(s):  
Jennifer Seelisch ◽  
Matthew Zatzman ◽  
Federico Comitani ◽  
Fabio Fuligni ◽  
Ledia Brunga ◽  
...  

10030 Background: Infant acute lymphoblastic leukemia (ALL) is the only subtype of childhood ALL whose outcome has not improved over the past two decades. The most important prognosticator is the presence of rearrangements in the Mixed Lineage Leukemia gene (MLL-r), however, many patients present with high-risk clinical features but without MLL-r. We recently identified two cases of infant ALL with high-risk clinical features resembling MLL-r, but were negative for MLL-r by conventional diagnostics. RNA sequencing revealed a partial tandem duplication in MLL (MLL-PTD). We thus aimed to determine if MLL-PTD, other MLL abnormalities, or other genetic or transcriptomic features were driving this subset of high-risk infant ALL without MLL-r. Methods: We obtained 19 banked patient samples from the Children’s Oncology Group (COG) infant ALL trial (AALL0631) from MLL wildtype patients as determined by FISH and cytogenetics. Utilizing deep RNA-sequencing, we manually inspected the MLL gene for MLL-PTD, while also performing automated fusion detection and gene expression profiling in search of defining features of these tumors. Results: 3 additional MLL-PTDs were identified, all in patients with infant T-cell ALL, whereas both index cases were in patients with infant B-cell ALL. Gene expression profiling analysis revealed that all five MLL-PTD infants clustered together. Eight infants (7 with B-cell ALL) were found to have Ph-like expression. Five of these 8 infants were also found to have an IKZF1/JAK2 expression profile; one of these five had a PAX5-JAK2 fusion detected. Two infants (including the one noted above) had novel PAX5 fusions, known drivers of B-cell leukemia. Additional detected fusions included TCF3-PBX1 and TCF4-ZNF384. Conclusions: MLL-PTDs were found in both B- and T-cell infant ALL. Though Ph-like ALL has been described in adolescents and young adults, we found a substantial frequency of Ph-like expression among MLL-WT infants. Further characterization of these infants is ongoing. If replicated in other infant cohorts, these two findings may help explain the poor prognosis of MLL-WT ALL when compared to children with standard risk ALL, and offer the possibility of targeted therapy for select infants.


2009 ◽  
Vol 69 (5) ◽  
pp. 437-446 ◽  
Author(s):  
D. Brudzewsky ◽  
A. E. Pedersen ◽  
M. H. Claesson ◽  
M. Gad ◽  
N. N. Kristensen ◽  
...  

2013 ◽  
Vol 56 (4) ◽  
pp. 530 ◽  
Author(s):  
Yan Zheng ◽  
Yuanyuan Zha ◽  
Robbert M. Spaapen ◽  
Rebecca Mathew ◽  
Kenneth Barr ◽  
...  

2007 ◽  
Vol 179 (11) ◽  
pp. 7406-7414 ◽  
Author(s):  
Victor Appay ◽  
Andreas Bosio ◽  
Stefanie Lokan ◽  
Yvonne Wiencek ◽  
Christian Biervert ◽  
...  

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 2277-2277
Author(s):  
Daruka Mahadevan ◽  
Catherine Spier ◽  
Kimiko Della Croce ◽  
Susan Miller ◽  
Benjamin George ◽  
...  

Abstract Background: WHO classifies NHL into B (~85%) and T (~15%) cell subtypes. Of the T-cell NHL, peripheral T-cell NHL (PTCL, NOS) comprises ~6–10% with an inferior response and survival to chemotherapy compared to DLBCL. Gene Expression Profiling (GEP) of DLBCL has provided molecular signatures that define 3 subclasses with distinct survival rates. The current study analyzed transcript profiling in PTCL (NOS) and compared and contrasted it to GEP of DLBCL. Methods : Snap frozen samples of 5 patients with PTCL (NOS) and 4 patients with DLBCL were analyzed utilizing the HG-U133A 2.0 Affymetrix array (~18,400 transcripts, 22,000 probe sets) after isolating and purifying total RNA (Qiagen, RNAeasy). The control RNA samples were isolated from normal peripheral blood (PB) B-cell (AllCell, CA), normal PB T-cell (AllCell, CA) and normal lymph node (LN). Immunohisto-chemistry (IHC) confirmed tumor lineage and quantitative real time RT-PCR was performed on selected genes to validate the microarray study. The GEP data were processed and analyzed utilizing Affymetrix MAS 5.0 and GeneSpring 5.0 software. Our data were analyzed in the light of the published GEP of DLBCL (lymphochip and affymtrix) and the validated 10 prognostic genes (by IHC and real time RT-PCR). Results : Data are represented as “robust” increases or decreases of relative gene expression common to all 5 PTCL or 4 DLBCL patients respectively. The table shows the 5 most over-expressed genes in PTCL or DLBCL compared to normal T-cell (NT), B-cell (NB) and lymph node (LN). PTCL vs NT PTCL vs LN DLVCL vs NB DLBCL vs LN COL1A1 CHI3L1 CCL18 CCL18 CCL18 CCL18 VNN1 IGJ CXCL13 CCL5 UBD VNN1 IGFBP7 SH2D1A LYZ CD52 RARRES1 NKG7 CCL5 MAP4K1 Of the top 20 increases, 3 genes were common to PTCL and DLBCL when compared to normal T and B cells, while 11 were common when compared to normal LN. Comparison of genes common to normal B-cell and LN Vs DLBCL or PTCL and normal T-cell and LN Vs PTCL or DLBCL identified sets of genes that are commonly and differentially expressed in PTCL and/or DLBCL. The 4 DLBCL patients analyzed express 3 of 10 prognostic genes compared to normal B-cells and 7 of 10 prognostic genes compared to normal LN and fall into the non-germinal center subtype. Quantitative real time RT-PCR on 10 functionally distinct common over-expressed genes in the 5 PTCL (NOS) patients (Lumican, CCL18, CD14, CD54, CD106, CD163, α-PDGFR, HCK, ABCA1 and Tumor endothelial marker 6) validated the microarray data. Conclusions: GEP of PTCL (NOS) and DLBCL in combination with quantitative real time RT-PCR and IHC have identified a ‘molecular signature’ for PTCL and DLBCL based on a comparison to normal (B-cell, T-cell and LN) tissue. The categorization of the GEP based on the six hallmarks of cancer identifies a ‘tumor profile signature’ for PTCL and DLBCL and a number of novel targets for therapeutic intervention.


Oncogene ◽  
2005 ◽  
Vol 25 (10) ◽  
pp. 1560-1570 ◽  
Author(s):  
B Ballester ◽  
O Ramuz ◽  
C Gisselbrecht ◽  
G Doucet ◽  
L Loï ◽  
...  

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